Models of biochemical systems presented as a set of formal reaction rules with kinetic expressions can be interpreted with different semantics: as either deterministic Ordinary Differential Equations, stochastic continuous-time Markov Chains, Petri nets or Boolean transition systems. While the formal composition of reaction models can be syntactically defined as the (multiset) union of the reac...

We present a scalable hybrid systems modeling framework to describe the flow of traffic in communication networks. To characterize network behavior, these models use averaging to continuously approximate discrete variables such as congestion window and queue size. Because averaging occurs over short time intervals, one still models discrete events such as the occurrence of a drop and the conseq...

Hybrid simulation involves the use of multiple simulation paradigms, and is becoming an increasingly common approach to modelling modern, complex systems. Despite growing interest in its use, little guidance exists for modellers regarding the nature and variety of hybrid simulation models. Here, we concentrate on one particular hybrid – that involving agent-based and system dynamics models. Bas...

A hybrid system or hybrid intelligent system uses the approach of integrating different learning or decision-making models. Each learning model works in a different manner and exploits different set of features. Integrating different learning models gives better performance than the individual learning or decisionmaking models by reducing their individual limitations and exploiting their differ...

We use a Markov Chain Monte Carlo (MCMC) MML algorithm to learn hybrid Bayesian networks from observational data. Hybrid networks represent local structure, using conditional probability tables (CPT), logit models, decision trees or hybrid models, i.e., combinations of the three. We compare this method with alternative local structure learning algorithms using the MDL and BDe metrics. Results a...

Hybrid systems have received a lot of attention in the past decade and a number of different models have been proposed in order to establish mathematical framework that is able to handle both continuous and discrete aspects. This contribution is focused on two models: hybrid automata and hybrid control systems with continuousdiscrete interface and the importance of clock models is emphasized. S...

One purpose of this paper is to propose the hybrid neural network models for bankruptcy prediction. The proposed hybrid neural network models are, respectively, a MDA model integrated with financial ratios, a MDA model integrated with financial ratios and intellectual capital ratios, a MDA-assisted neural network model integrated with financial ratios, and a MDA-assisted neural network model in...

The development of hybrid models represents an important step in energy economy modeling, as hybrid models embody the most useful features of both top-down and bottomup models. Hybrid models explicitly represent technologies in a similar way to bottom-up models, thus enabling policy makers to understand the effects of technology-specific policies on energy consumption and the economy. However, ...